Abstract
For the sake of measuring the reliability of actual face recognition system with continuous variables, after analyzing system structure, common failures, influencing factors of reliability, and maintenance data of a public security face recognition system in use, we propose a reliability evaluation model based on Continuous Bayesian Network. We design a Clique Tree Propagation algorithm to reason and solve the model, which is realized by R programs, and as a result, the reliability coefficient of the actual system is obtained. Subsequently, we verify the Continuous Bayesian Network by comparing its evaluation results with those of traditional Bayesian Network and Ground Truth. According to these evaluation results, we find out some weaknesses of the system and propose some optimization strategies by the way of finding the right remedies and filling in blanks. In this paper, we synthetically apply a variety of methods, such as qualitative analysis, quantitative analysis, theoretical analysis, and empirical analysis, to solve the unascertained causal reasoning problem. The evaluation method is reasonable and valid, the results are consistent with realities and objective, and the proposed strategies are very operable and targeted. This work is of theoretical significance to research on reliability theory. It is also of practical significance to the improvement of the system’s reliability and the ability of public order maintenance.
Highlights
Research ArticleZhiqiang Liu ,1,2,3 Hongzhou Zhang ,1 Shengjin Wang, Weijun Hong, Jianhui Ma, and Yanfeng He5
For the sake of measuring the reliability of actual face recognition system with continuous variables, after analyzing system structure, common failures, influencing factors of reliability, and maintenance data of a public security face recognition system in use, we propose a reliability evaluation model based on Continuous Bayesian Network
We synthetically apply a variety of methods, such as qualitative analysis, quantitative analysis, theoretical analysis, and empirical analysis, to solve the unascertained causal reasoning problem. e evaluation method is reasonable and valid, the results are consistent with realities and objective, and the proposed strategies are very operable and targeted. is work is of theoretical significance to research on reliability theory
Summary
Zhiqiang Liu ,1,2,3 Hongzhou Zhang ,1 Shengjin Wang, Weijun Hong, Jianhui Ma, and Yanfeng He5. E control subsystem can mainly implement equipment management, authority management, scheduling code stream, switching video signal, controlling transmission, controlling network, controlling storage, and sending various application instructions It is a core part of a public security face recognition system. Its advantages are as follows: (1) the Continuous Bayesian Network is a visualized model of representing unascertained causal relationship, whose ability to process unascertained information is very high; (2) the Continuous Bayesian Network can describe continuous variables; (3) the Continuous Bayesian Network can fuse multi-source information effectively; (4) the Continuous Bayesian Network modeling is simple, while reasoning objectively Because of these advantages, this paper constructs a Continuous Bayesian Network model to measure the reliability of the public security face recognition system. Geer and Klir [12] propose “Information preserving transformation” in the process of transformation between probability and possibility, that is, the uncertainty in information remains unchanged in the process of mutual transformation between two theories. eir proposed conversion equations are as follows:
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